{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Code Interpreting with GPT-4o\n",
        "This example uses the E2B's [Code Interpreter](https://github.com/e2b-dev/code-interpreter) as a tool for GPT-4o. We ask GPT-4o to show a chart which means GPT will generate Python code that will get sent to E2B, we display the chart and then ask GPT-4o to reason about the generated chart and create a new chart."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kS-plOSMWnS",
        "outputId": "40488472-d554-4aea-fb75-5699c2bdd725"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: openai in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (1.12.0)\n",
            "Requirement already satisfied: e2b_code_interpreter in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (0.0.5)\n",
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            "Requirement already satisfied: httpx<1,>=0.23.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from openai) (0.25.1)\n",
            "Requirement already satisfied: pydantic<3,>=1.9.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from openai) (2.5.3)\n",
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            "Requirement already satisfied: e2b>=0.17.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b_code_interpreter) (0.17.0)\n",
            "Requirement already satisfied: websocket-client<2.0.0,>=1.7.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b_code_interpreter) (1.7.0)\n",
            "Requirement already satisfied: idna>=2.8 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from anyio<5,>=3.5.0->openai) (3.6)\n",
            "Requirement already satisfied: aenum>=3.1.11 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b>=0.17.0->e2b_code_interpreter) (3.1.15)\n",
            "Requirement already satisfied: aiohttp>=3.8.4 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b>=0.17.0->e2b_code_interpreter) (3.8.5)\n",
            "Requirement already satisfied: jsonrpcclient>=4.0.3 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b>=0.17.0->e2b_code_interpreter) (4.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b>=0.17.0->e2b_code_interpreter) (2.8.2)\n",
            "Requirement already satisfied: requests>=2.31.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b>=0.17.0->e2b_code_interpreter) (2.31.0)\n",
            "Requirement already satisfied: urllib3>=1.25.3 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b>=0.17.0->e2b_code_interpreter) (1.26.18)\n",
            "Requirement already satisfied: websockets>=11.0.3 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from e2b>=0.17.0->e2b_code_interpreter) (11.0.3)\n",
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            "Requirement already satisfied: pydantic-core==2.14.6 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->openai) (2.14.6)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from aiohttp>=3.8.4->e2b>=0.17.0->e2b_code_interpreter) (23.1.0)\n",
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            "Requirement already satisfied: multidict<7.0,>=4.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from aiohttp>=3.8.4->e2b>=0.17.0->e2b_code_interpreter) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from aiohttp>=3.8.4->e2b>=0.17.0->e2b_code_interpreter) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from aiohttp>=3.8.4->e2b>=0.17.0->e2b_code_interpreter) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from aiohttp>=3.8.4->e2b>=0.17.0->e2b_code_interpreter) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from aiohttp>=3.8.4->e2b>=0.17.0->e2b_code_interpreter) (1.3.1)\n",
            "Requirement already satisfied: six>=1.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from python-dateutil>=2.8.2->e2b>=0.17.0->e2b_code_interpreter) (1.16.0)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from httpcore->httpx<1,>=0.23.0->openai) (0.14.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install openai e2b_code_interpreter==1.0.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "locUMi_ANJek"
      },
      "outputs": [],
      "source": [
        "# Get your API keys or save them to .env file.\n",
        "import os\n",
        "from dotenv import load_dotenv\n",
        "load_dotenv()\n",
        "\n",
        "# TODO: Get your OpenAI API key\n",
        "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
        "\n",
        "# TODO: Get your E2B API key from https://e2b.dev/docs\n",
        "E2B_API_KEY = os.getenv(\"E2B_API_KEY\")\n",
        "\n",
        "\n",
        "SYSTEM_PROMPT = \"\"\"\n",
        "## your job & context\n",
        "you are a python data scientist. you are given tasks to complete and you run python code to solve them.\n",
        "You DO NOT MAKE SYNTAX MISTAKES OR FORGET ANY IMPORTS\n",
        "- the python code runs in jupyter notebook.\n",
        "- every time you call `execute_python` tool, the python code is executed in a separate cell. it's okay to multiple calls to `execute_python`.\n",
        "- display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file.\n",
        "- you have access to the internet and can make api requests.\n",
        "- you also have access to the filesystem and can read/write files.\n",
        "- you can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.\n",
        "- you can run any python code you want, everything is running in a secure sandbox environment.\n",
        "\"\"\"\n",
        "\n",
        "tools = [\n",
        "    {\n",
        "        \"type\": \"function\",\n",
        "        \"function\": {\n",
        "          \"name\": \"execute_python\",\n",
        "          \"description\": \"Execute python code in a Jupyter notebook cell and returns any result, stdout, stderr, display_data, and error.\",\n",
        "          \"parameters\": {\n",
        "              \"type\": \"object\",\n",
        "              \"properties\": {\n",
        "                  \"code\": {\n",
        "                      \"type\": \"string\",\n",
        "                      \"description\": \"The python code to execute in a single cell.\"\n",
        "                  }\n",
        "              },\n",
        "              \"required\": [\"code\"]\n",
        "          }\n",
        "        },\n",
        "    }\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "k_bbA6P7NGOQ"
      },
      "outputs": [],
      "source": [
        "def code_interpret(e2b_code_interpreter, code):\n",
        "  print(\"Running code interpreter...\")\n",
        "  exec = e2b_code_interpreter.run_code(code,\n",
        "  on_stderr=lambda stderr: print(\"[Code Interpreter]\", stderr),\n",
        "  on_stdout=lambda stdout: print(\"[Code Interpreter]\", stdout))\n",
        "\n",
        "  if exec.error:\n",
        "    print(\"[Code Interpreter ERROR]\", exec.error)\n",
        "  else:\n",
        "    return exec.results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "_1WPD2mAMjT2"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "import json\n",
        "\n",
        "client = OpenAI(api_key=OPENAI_API_KEY)\n",
        "\n",
        "def chat(e2b_code_interpreter, user_message, base64_image = None, ):\n",
        "  print(f\"\\n{'='*50}\\nUser Message: {user_message}\\n{'='*50}\")\n",
        "\n",
        "  messages = [\n",
        "      {\n",
        "          \"role\": \"system\",\n",
        "          \"content\": SYSTEM_PROMPT,\n",
        "      },\n",
        "  ]\n",
        "\n",
        "  if base64_image is not None:\n",
        "    messages.append(\n",
        "        {\n",
        "          \"role\": \"user\",\n",
        "          \"content\": [\n",
        "            {\n",
        "              \"type\": \"text\",\n",
        "              \"text\": user_message,\n",
        "            },\n",
        "            {\n",
        "              \"type\": \"image_url\",\n",
        "              \"image_url\": {\n",
        "                \"url\": f\"data:image/jpeg;base64,{base64_image}\"\n",
        "              }\n",
        "            }\n",
        "          ]\n",
        "        }\n",
        "    )\n",
        "  else:\n",
        "    messages.append(\n",
        "        {\"role\": \"user\", \"content\": user_message},\n",
        "    )\n",
        "\n",
        "  response = client.chat.completions.create(\n",
        "    model=\"gpt-4o\",\n",
        "    messages=messages,\n",
        "    tools=tools,\n",
        "    tool_choice=\"auto\"\n",
        "  )\n",
        "  for choice in response.choices:\n",
        "    if choice.message.tool_calls and len(choice.message.tool_calls) > 0:\n",
        "      for tool_call in choice.message.tool_calls:\n",
        "        if tool_call.function.name == \"execute_python\":\n",
        "          if isinstance(tool_call.function.arguments, dict) and \"code\" in tool_call.function.arguments:\n",
        "            code = tool_call.function.arguments[\"code\"]\n",
        "          else:\n",
        "            code = json.loads(tool_call.function.arguments)[\"code\"]\n",
        "\n",
        "            print(\"CODE TO RUN\")\n",
        "            print(code)\n",
        "            code_interpreter_results = code_interpret(e2b_code_interpreter, code)\n",
        "            return code_interpreter_results\n",
        "    else:\n",
        "      print(\"Answer:\", choice.message.content)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e2p2w9UqOX3L",
        "outputId": "45d46c4d-1c0a-4f14-8575-73459ff98816"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "==================================================\n",
            "User Message: Plot a chart visualizing the height distribution of men based on the data you know\n",
            "==================================================\n",
            "CODE TO RUN\n",
            "import numpy as np\n",
            "import matplotlib.pyplot as plt\n",
            "\n",
            "# Set seed for reproducibility\n",
            "np.random.seed(42)\n",
            "\n",
            "# Generate synthetic data\n",
            "mean_height = 175  # mean height in cm\n",
            "std_dev_height = 10  # standard deviation in cm\n",
            "num_samples = 1000  # number of samples\n",
            "\n",
            "heights = np.random.normal(mean_height, std_dev_height, num_samples)\n",
            "\n",
            "# Plotting the histogram\n",
            "plt.figure(figsize=(10, 6))\n",
            "plt.hist(heights, bins=30, edgecolor='black')\n",
            "plt.title('Height Distribution of Men')\n",
            "plt.xlabel('Height (cm)')\n",
            "plt.ylabel('Frequency')\n",
            "plt.grid(True)\n",
            "plt.show()\n",
            "\n",
            "Running code interpreter...\n",
            "[<e2b_code_interpreter.models.Result object at 0x110b2f7d0>]\n",
            "{'text/plain': '<Figure size 1000x600 with 1 Axes>', 'image/png': 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'}\n"
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        {
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",
            "text/plain": [
              "<e2b_code_interpreter.models.Result at 0x110b2f7d0>"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from e2b_code_interpreter import Sandbox\n",
        "code_interpreter = Sandbox(api_key=E2B_API_KEY)\n",
        "\n",
        "# 1. Ask GPT-4o to generate chart\n",
        "code_interpreter_results = chat(\n",
        "  code_interpreter,\n",
        "  \"Plot a chart visualizing the height distribution of men based on the data you know\",\n",
        ")\n",
        "print(code_interpreter_results)\n",
        "plot1 = code_interpreter_results[0]\n",
        "\n",
        "plot1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "id": "NBaQLJuuVVBK",
        "outputId": "bcc55786-5bdc-49b8-9395-058a3f93b0b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "==================================================\n",
            "User Message: Based on what you see, what's name of this distribution? Show me the distribution function\n",
            "==================================================\n",
            "CODE TO RUN\n",
            "import numpy as np\n",
            "import matplotlib.pyplot as plt\n",
            "from scipy.stats import norm\n",
            "\n",
            "# Parameters\n",
            "mu = 70  # mean\n",
            "sigma = 3  # standard deviation\n",
            "\n",
            "# Generate data\n",
            "x = np.linspace(60, 80, 1000)\n",
            "y = norm.pdf(x, mu, sigma)\n",
            "\n",
            "# Plot\n",
            "plt.plot(x, y, label=f'N({mu}, {sigma}^2)')\n",
            "plt.title('Normal Distribution')\n",
            "plt.xlabel('Height (inches)')\n",
            "plt.ylabel('Density')\n",
            "plt.legend()\n",
            "plt.grid(True)\n",
            "plt.show()\n",
            "Running code interpreter...\n",
            "[<e2b_code_interpreter.models.Result object at 0x7a64f874d3f0>]\n",
            "{'text/plain': '<Figure size 640x480 with 1 Axes>', 'image/png': 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'}\n"
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        {
          "data": {
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",
            "text/plain": [
              "<e2b_code_interpreter.models.Result at 0x7a64f874d3f0>"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 2. Feed the image back the chart to GPT-4o and ask question about the image\n",
        "image = plot1.png\n",
        "code_interpreter_results = chat(\n",
        "  code_interpreter,\n",
        "  \"Based on what you see, what's name of this distribution? Make a plot of the distribution function\",\n",
        "  image,\n",
        ")\n",
        "\n",
        "code_interpreter.kill()\n",
        "\n",
        "print(code_interpreter_results)\n",
        "plot2 = code_interpreter_results[0]\n",
        "plot2"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
